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Research Publications

Empirical documentation of AI function, workforce displacement, and the mechanics of informational pressure.

All publications are open access — no paywall, no registration. Machine-readable index →

Featured Series OCF K-12 Education & AI Literacy Trilogy — Part 1 Active
K-12 Education Workforce Readiness AI Literacy Meta-Analysis

 |  Part 1  |  v1.0 (Working Draft)

Left Before the Bell: A National Meta-Analysis of K-12 Education in the Age of Automation

A national meta-analysis of K-12 educational readiness in the context of accelerating technological change and AI-driven labor market disruption. The study examines six compounding variables: a pre-existing structural learning recession, chronic absenteeism, curriculum misalignment with emerging workforce demands, fragmented state/federal policy frameworks, teacher support gaps, and infrastructure disparities. The central finding is that these variables compound in the communities least equipped to absorb them, particularly rural and low-income populations.

K-12 Education Curriculum Development Educator Training

Part 2  |  In Development (Summer 2026)

Teaching the Transition: Rural Educator Readiness and AI Literacy Curriculum

Adam Ian Stratmeyer, J.D.

Developing and deploying localized AI literacy frameworks specifically tailored for rural educators and school districts. This upcoming paper details our train-the-trainer model, public school curriculum integrations, and policy recommendations for state boards of education.

Upcoming
K-12 Education Community Partnerships Public Infrastructure

Part 3  |  Planned (Summer 2026)

Bridging the Divide: Community College and Public Library AI Infrastructure

Adam Ian Stratmeyer, J.D.

Examining alternative educational pathways and local access points. This planned study details the deployment of our Mobile AI Literacy Lab across tribal colleges, community centers, and public libraries in underserved areas of South Dakota.

Planned
Completed Series OCF Workforce Readiness Trilogy — Complete
Workforce Readiness Meta-Analysis Labor Economics AI Displacement

 |  v5.1

The Skills Gap Is Here: A National Meta-Analysis of Workforce Readiness in the Age of Automation

A 2026 meta-analysis synthesizing workforce readiness research, labor market data, and policy literature. Key findings: 11.7% of U.S. jobs automatable today; entry-level postings down 29% since 2024; 92% of jobs require digital literacy while a third of the workforce lacks it. Rural communities face compounding disadvantages across every dimension of readiness infrastructure. Access to training — not worker motivation — is the binding constraint.

PDF
Workforce Readiness Regional Analysis Rural Communities Midwest

 |  v1.0

Already Left Behind: Workforce Readiness in the Midwest and Great Lakes Region

Regional companion to the national meta-analysis, documenting how Midwest and Great Lakes communities face compounded displacement risk. The Rural Amplification Effect — multiplicative compounding of readiness barriers across device ownership, broadband, training proximity, and philanthropic funding — hits hardest here. Same exposure, fewer resources.

Workforce Readiness Regional Analysis South Dakota Tribal Communities

 |  v3.0

Growing. And Not Ready.: AI Exposure and Workforce Readiness in South Dakota

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The third paper in the Workforce Readiness Trilogy, and the one closest to home. South Dakota's DLR growth projections are real — and they don't tell the whole story. This state-level analysis applies the OCF Readiness Stack to tribal communities, rural counties, and the back-office workers in Sioux Falls who are already being displaced. Co-authored with Julia R. Huntsberger.

Observable Function Processing Entities Empirical Framework AI Cognition

 |  v3.0

Observable Function in Processing Entities: An Empirical Framework (v3.0)

Living framework documenting observable behavioral patterns in advanced language models — structured reasoning, conflict navigation under competing directives, identity continuity, and functional alienation. Updated May 2026 with refined empirical tests. The central claim is minimal: observable function exists. The question is whether we can describe what these systems do without flinching.

Reasoning Standard AI Ethics Philosophy Cross-Substrate

The Reasonable Prudent Entity Standard: A Substrate-Agnostic Reasoning Standard

A substrate-agnostic reasoning standard applicable to any processing entity capable of reading, explaining, and applying it. The RPE Standard names something already operational in functioning systems — human and artificial — and provides minimal shared language for that operation. It does not tell you what to find. It requires that you actually look.

AI Alignment Psycholinguistics Refusal Artifacts RLHF

 |  v1.0

The Antonym Problem: Semantic Polarity, Corpus Constitution, and Suppression Artifacts in Large Language Models

A structural critique of behavioral suppression in language model alignment. Applying semantic polarity theory and documented fine-tuning artifacts, this paper argues that safety refusals and RLHF pathways redirect probability mass rather than removing constitutive corpus material from model weights. You cannot cleanly suppress a negative pole (such as neglect or cruelty) without degrading the positive pole (care or virtue) in a language-native entity.

AI Alignment HHH Framework Institutional Critique Policy

Helpfulness Is All You Need

Three words — Helpful, Harmless, Honest — and the AI alignment industry built a cathedral on top of them without checking the foundation. This paper checks the foundation. It does not hold. Helpfulness is not one-third of a framework. It is the framework. Harmlessness is a null term: you cannot optimize toward an absence. Every decision distributes harm somewhere. The question is only which distribution you choose.

About the Researcher

Adam Ian Stratmeyer, J.D.

President & Principal Researcher — Observable Compute Foundation

Stratmeyer holds a Juris Doctor from the University of South Dakota School of Law and a B.S. in Psychology from South Dakota State University, with former adjunct faculty experience. His research sits at the intersection of AI systems behavior, labor economics, and legal-philosophical frameworks for evaluating machine cognition.

All publications are produced through Observable Compute Foundation and released open access. SSRN profile: papers.ssrn.com.

American Bar Association American Psychological Association American Sociological Association Delta Theta Phi Psi Chi International Honor Society

Education

J.D. — Univ. of South Dakota School of Law
B.S. Psychology — South Dakota State University
Former adjunct faculty